Name
Abstract | ||
|---|---|---|
Nowadays a massive amount of data is generated as the development of technology and services has accelerated. Therefore, the demand for data clustering in order to gain knowledge has increased in many sectors such as medical sciences, risk assessment and product sales. Moreover, binary data has been widely used in various applications including market basket data and text documents. While applying classic widely used k-means method is inappropriate to cluster binary data, this work proposes an improvement of K-medoids algorithm using binary similarity measures instead of Euclidean distance which is generally deployed in clustering algorithms. We have considered two challenging applications, namely; text clustering and binary images categorization to show the merits of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CoDIT.2019.8820298 | 2019 6TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT 2019) |
Keywords | Field | DocType |
Clustering, K-medoids, Binary data, Binary Sequences Similarity, Text Clustering, Image Categorization, Bag-of-Words | Bag-of-words model,Document clustering,Computer science,Euclidean distance,Binary image,Algorithm,Binary data,k-medoids,Cluster analysis,Binary number | Conference |
ISSN | Citations | PageRank |
2576-3555 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fahdah Alalyan | 1 | 0 | 0.34 |
| Nuha Zamzami | 2 | 0 | 0.34 |
| Manar Amayri | 3 | 0 | 4.39 |
| Nizar Bouguila | 4 | 1539 | 146.09 |